We use a supervised learning algorithm to predict the correlation matrices and an unsupervised learning algorithm to analyze the results. The machine learning model used to predict the correlation matrix is the feed-forward neural network referred to as multi-layer perceptron [1]}. The architecture of this model contains one input layer, two hidden layers, and one output layer (Fig. REF ). A layer comprises several neurons, and neurons in the adjacent layers are connected. We adopt the SELU (scaled exponential linear unit) as a basic activation function except for the output layer [2]}. We use sigmoid as the output activation function when the desired output lies between 0 and 1, and \(\tanh \)  when it is between \(-1\)  and 1. The relationship between the input (\(a_{j}^{(\ell -1)}\) ) and the output (\(a_{i}^{(\ell )}\) ) of a layer can be given by,
\( z^{(\ell )}_{i}=\sum ^{K}_{j=1}w^{(\ell -1)}_{ji}a^{(\ell -1)}_{j},\;\;\;a^{(\ell )}_{i} = \textrm {SELU}(z^{(\ell )}_{i})\) 
